The papers [1]} and [2]} define the backgrounds of the One Class SVM. It defines this problem as the SVM model applied on the unlabeled data and it describes the algorithm and its optimization in detail. The implementation of this method can be found besides others in the Novelty and Outlier detection scikit-learn Python package. This method is quite efficient, but it often fails on the high dimensional data, as it is described in [3]}. Despite this factor, it is usually used only as a classification layer for a reduced feature space, rather than for straight use with image data.
